The entire process of building a machine learning model for credit card fraud detection. The goal is to develop a model that can accurately classify transactions as either normal or abnormal. This would involve end-to-end model training to create the best possible classifier. In this python machine learning project, we built a binary classifier using the Random Forest algorithm to detect credit card fraud transactions. Through this project, we understood and applied techniques to address the class imbalance issues and achieved an accuracy of more than 99%.
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